Vidal-Saez Maria Sol, Garcia-Ojalvo Jordi
Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park, Dr Aiguader 88, Barcelona, 08003 Spain.
Biophys Rev. 2025 Mar 3;17(2):259-269. doi: 10.1007/s12551-025-01295-w. eCollection 2025 Apr.
Recurrent neural networks are frequently studied in terms of their information-processing capabilities. The structural properties of these networks are seldom considered, beyond those emerging from the connectivity tuning necessary for network training. However, real biological networks have non-contingent architectures that have been shaped by evolution over eons, constrained partly by information-processing criteria, but more generally by fitness maximization requirements. Here, we examine the topological properties of existing biological networks, focusing in particular on gene regulatory networks in bacteria. We identify structural features, both local and global, that dictate the ability of recurrent networks to store information on the fly and process complex time-dependent inputs.
循环神经网络经常从其信息处理能力的角度进行研究。除了网络训练所需的连接性调整所产生的那些特性外，这些网络的结构特性很少被考虑。然而，真实的生物网络具有非偶然的架构，这些架构是经过漫长的进化形成的，部分受到信息处理标准的限制，但更普遍地受到适应性最大化要求的限制。在这里，我们研究现有生物网络的拓扑特性，特别关注细菌中的基因调控网络。我们确定了局部和全局的结构特征，这些特征决定了循环网络即时存储信息和处理复杂时间相关输入的能力。
